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A novel feature selection for evolving compact dispatching rules using genetic programming for dynamic job shop scheduling
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AI
DOI：10.1080/00207543.2022.2053603.png)
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Because of advances in computational power and machine learning algorithms, the automated design of scheduling rules using Genetic Programming (GP) is successfully applied to solve dynamic job shop scheduling problems. Although GP-evolved rules usually outperform dispatching rules reported in the literature, intensive computational costs and rule interpretability persist as important limitations. Furthermore, the importance of features in the terminal set varies greatly among scenarios. The inclusion of irrelevant features broadens the search space. Therefore, proper selection of features is necessary to increase the convergence speed and to improve rule understandability using fewer features. In this paper, we propose a new representation of the GP rules that abstracts the importance of each terminal. Moreover, an adaptive feature selection mechanism is developed to estimate terminals' weights from earlier generations in restricting the search space of the current generation. The proposed approach is compared with three GP algorithms from the literature and 30 human-made rules from the literature under different job shop configurations and scheduling objectives, including total weighted tardiness, mean tardiness, and mean flow time. Experimentally obtained results demonstrate that the proposed approach outperforms methods from the literature in generating more interpretable rules in a shorter computational time without sacrificing solution quality.
Keyword:
Discrete event simulation
dispatching rules
dynamic job shop scheduling
feature selection
genetic programming
期刊
IF:
7.3
论文数:
1.1W
被引数:
3.7W
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分享收藏A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions在不同的流程时间和迟到相关目标函数下，作业车间调度问题的优先级规则的比较
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